3 citations · 7 across the 8 of their papers we have counts for
8 papers
EXPLAIN Yourself! Finding Query Planner Stalls Across DBMSes
Geoffrey X. Yu, Ryan Marcus, Tim Kraska
Query planners are typically expected to produce optimized plans quickly, leading many researchers (including the authors of this paper) and practitioners to design systems that as…
Tailwind: A Practical Framework for Query Accelerators
Geoffrey X. Yu, Ryan Marcus, Tim Kraska
Relational database management systems (RDBMSes) can process general-purpose queries, but often have lower performance compared to custom-built solutions for specific queries. For…
Data-Agnostic Cardinality Learning from Imperfect Workloads
Peizhi Wu, Rong Kang, Tieying Zhang +3
Cardinality estimation (CardEst) is a critical aspect of query optimization. Traditionally, it leverages statistics built directly over the data. However, organizational policies (…
Low Rank Learning for Offline Query Optimization
Zixuan Yi, Yao Tian, Zachary G. Ives +1
Recent deployments of learned query optimizers use expensive neural networks and ad-hoc search policies. To address these issues, we introduce \textsc{LimeQO}, a framework for offl…
The Unreasonable Effectiveness of LLMs for Query Optimization
Peter Akioyamen, Zixuan Yi, Ryan Marcus
Recent work in database query optimization has used complex machine learning strategies, such as customized reinforcement learning schemes. Surprisingly, we show that LLM embedding…
A Practical Theory of Generalization in Selectivity Learning
Peizhi Wu, Haoshu Xu, Ryan Marcus +1
Query-driven machine learning models have emerged as a promising estimation technique for query selectivities. Yet, surprisingly little is known about the efficacy of these techniq…
Stage: Query Execution Time Prediction in Amazon Redshift
Ziniu Wu, Ryan Marcus, Zhengchun Liu +7
Query performance (e.g., execution time) prediction is a critical component of modern DBMSes. As a pioneering cloud data warehouse, Amazon Redshift relies on an accurate execution…
Adding Domain Knowledge to Query-Driven Learned Databases
Peizhi Wu, Ryan Marcus, Zachary G. Ives
In recent years, \emph{learned cardinality estimation} has emerged as an alternative to traditional query optimization methods: by training machine learning models over observed qu…